Felipe Bravo-Marquez

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 2 papers; trusts 25 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #14,433 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 10 citations into it; #2,681 of 5,688 by trust here, #3,235 by citations
• Information Systems — 8 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Felipe Bravo-Marquez trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Saif M. Mohammad
2. Tong Zhang
3. Minqing Hu
4. Svetlana Kiritchenko
5. Andrea Esuli
6. Finn Årup Nielsen
7. Soo-Min Kim
8. Lei Zhang
9. Albert Bifet
10. Theresa Wilson

Who trusts Felipe Bravo-Marquez overall

1. Saif M. Mohammad
2. Endang Wahyu Pamungkas
3. Svetlana Kiritchenko
4. Karolina Stańczak
5. Chris Sweeney
6. Jayadev Bhaskaran
7. Francesco Barbieri
8. Francisca Adoma Acheampong
9. Rishabh Bhardwaj
10. Paula Czarnowska

Papers from Felipe Bravo-Marquez's perspective

Ordered by how strongly the researchers Felipe Bravo-Marquez trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• SemEval-2018 Task 1: Affect in Tweets (2018) — 940 citations
• Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets (2015) — 33 citations